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A novel adaptive moving average method for signal denoising in strong noise background

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Abstract

The moving average (MA) method has been widely used in signal processing, but it has problems of the dead zone and the fixed window. In this paper, an adaptive moving average (AMA) filtering method is proposed, which can sniff the inherent characteristics of the signal and assign time-varying optimal parameters to signal processing, hence solve dead zone and the fixed window problems. Firstly, this paper builds the theoretical framework of AMA, including the trial steps and optimization of the necessary parameters. To verify the effectiveness of the AMA, three signal processing methods are taken as comparison methods to process the noisy simulation and experimental signals. Comparison methods includes the MA, variational mode decomposition (VMD), and wavelet threshold denoising (WTD). Signals processed include linear frequency modulation (LFM) simulation signal, aperiodic square wave (ASW) simulation signal, LFM experimental signal produced by a signal generator, and nondestructive test signal of wire rope. Also, the output is analyzed qualitatively and quantitatively with signal-to-noise ratio (SNR), cross-correlation coefficient, amplitude error, and a newly defined local coincidence index. Compared to MA, VMD and WTD, the proposed AMA can solve the dead zone problem, recover noisy signal with higher SNR, cross-correlation coefficient, and lower amplitude error. These results indicate that AMA is a promising method in signal processing.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: All data included in this manuscript are available upon request by contacting with the corresponding author.]

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Acknowledgements

We acknowledge financial support by the National Natural Science Foundation of China (Grant No. 12072362), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF, EU) under Project No. PID2019-105554GB-I00.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Zhen Shan, Jianhua Yang & Houguang Liu

  2. Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, 28933, Móstoles, Madrid, Spain

    Miguel A. F. Sanjuán

  3. School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Chengjin Wu

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  1. Zhen Shan
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  2. Jianhua Yang
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  3. Miguel A. F. Sanjuán
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  4. Chengjin Wu
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  5. Houguang Liu
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Correspondence to Jianhua Yang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Shan, Z., Yang, J., Sanjuán, M.A.F. et al. A novel adaptive moving average method for signal denoising in strong noise background. Eur. Phys. J. Plus 137, 50 (2022). https://doi.org/10.1140/epjp/s13360-021-02279-x

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